Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool returns (dates and times for specific astronomical events), includes practical tips about event frequency and hemispheric differences, and provides an example of usage and output format. However, it doesn't mention potential limitations like computational constraints or error conditions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, returns, args, tips, example) and front-loaded with the core functionality. While comprehensive, some information in the 'Tips for LLMs' section (like the explanation of axial tilt) could be considered slightly beyond what's strictly necessary for tool invocation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (astronomical calculations with 3 parameters) and no annotations or output schema, the description provides substantial context including parameter details, return value explanation, usage tips, and an example. However, without an output schema, the description doesn't fully document the SeasonsResponse structure beyond listing its components.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by providing detailed parameter documentation in the 'Args' section. It explains each parameter's purpose (year to query, timezone offset, DST adjustment), provides value ranges (1700-2100 for year), and default behavior (UTC if timezone not provided). This adds significant meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get Earth's seasons and orbital events for a year' with specific resources (equinoxes, solstices, perihelion, aphelion). It distinguishes from siblings like get_moon_phases or get_planet_events by focusing exclusively on Earth's seasonal and orbital events.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool (for Earth's seasons and orbital events), but doesn't explicitly state when not to use it or name specific alternatives among siblings. The 'Tips for LLMs' section implies usage scenarios but doesn't provide explicit exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does an excellent job disclosing behavioral traits. It explains the return format (MoonPhasesResponse with phasedata list), time format (UT1, not local time), lunar cycle duration (29.5 days), and practical applications. The only minor gap is it doesn't mention error handling or rate limits, but otherwise provides comprehensive behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and appropriately sized. It starts with the core purpose, then provides usage context, followed by detailed parameter documentation, return format, practical tips, and a complete example. Every section adds value with zero redundant information, and the information is front-loaded with the most important details first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description provides complete context. It covers purpose, usage guidelines, parameter details, return format, time zone considerations, lunar cycle context, practical applications, and includes a working example. No additional information is needed for effective tool use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by providing detailed parameter semantics. It explains the date format (YYYY-MM-DD, no leading zeros required), valid range (1700-01-01 to 2100-12-31), num_phases range (1-99), default value (12), and relationship to lunar cycles (12 phases = 3 complete cycles, each cycle has 4 phases). This adds significant value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get upcoming moon phases starting from a given date' with specific details about what it returns (next N moon phase occurrences with exact times). It distinguishes from siblings by focusing specifically on moon phases rather than earth seasons, planet events, solar eclipses, or general sky/sun-moon data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: 'Useful for planning astronomical observations, photography, or understanding lunar cycles.' It also includes a 'Tips for LLMs' section with specific use cases (astronomy, photography, fishing/hunting, gardening) and alternative parameter values for different timeframes (num_phases=4 for next month, 12 for next quarter, 48 for next year).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does an excellent job describing what the tool returns (GeoJSON Feature with specific properties), edge cases ('Events may be empty if the planet doesn't rise/set that day'), and practical implications ('Transit time is when the planet is highest — best viewing time'). It also mentions artifact storage behavior when configured.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, args, returns, tips, example) and front-loaded with the core functionality. While comprehensive, every sentence earns its place by providing essential information. The example is particularly helpful for understanding usage patterns.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, astronomical calculations) and lack of both annotations and output schema, the description provides complete context. It explains what the tool does, how to use it, what parameters mean, what to expect in return, edge cases, and relationships to sibling tools. The return value documentation effectively substitutes for a missing output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by providing comprehensive parameter documentation. Each of the 6 parameters is clearly explained with examples, valid values, ranges, and practical meaning. The description adds significant value beyond what the bare schema provides, including enumerated planet names, date format examples, coordinate ranges, and timezone usage details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get rise, set, and transit times') and resources ('for a planet on a given day at a location'). It distinguishes from siblings by focusing specifically on planetary events rather than seasons, moon phases, positions, or other astronomical data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool versus alternatives: 'Use with get_planet_position to get full details at a specific time' directly names a sibling tool for complementary use. The 'Tips for LLMs' section offers additional context about when events might be empty and viewing characteristics of different planet types.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does an excellent job describing what the tool returns (altitude, azimuth, distance, etc.), interpretation of values (e.g., 'altitude > 0 means the planet is above the horizon'), and practical constraints ('Elongation < 10-15 degrees means planet is too close to the sun to see'). However, it doesn't mention potential limitations like computational complexity, error conditions, or rate limits, keeping it from a perfect score.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, returns, args, tips, example) and every sentence adds value. However, at approximately 400 words, it's quite lengthy for a tool description. While all content is useful, some information in the 'Tips for LLMs' section could potentially be streamlined or moved to documentation, preventing a perfect score for conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, astronomical calculations), no annotations, and no output schema, the description provides exceptional completeness. It explains what the tool does, when to use it, all parameter details, return data structure with field explanations, practical examples, and interpretation guidance. The example shows exactly how to invoke the tool and use the results, making this description self-contained and comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by providing comprehensive parameter semantics. Each of the 6 parameters is clearly explained with examples, valid ranges, and interpretation rules (e.g., 'timezone: Timezone offset from UTC in hours... When provided, the time parameter is interpreted as local time'). The planet parameter even lists all valid values (Mercury through Pluto), and date/time formats are specified with examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get position and observational data for a planet at a specific time and location.' It specifies the verb ('Get'), resource ('position and observational data for a planet'), and scope ('at a specific time and location'), distinguishing it from sibling tools like get_earth_seasons or get_moon_phases. The detailed return data list further clarifies the exact information provided.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance with named alternatives and specific contexts. It states: 'Essential for planning astronomical observations and answering "where is [planet] tonight?" questions,' and includes practical tips like 'For "where is Mars tonight?", use time="21:00" with appropriate timezone.' This clearly indicates when to use this tool versus other astronomical tools in the sibling set.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden and provides substantial behavioral context: it explains what data is returned (planets, moon phase, darkness indicator), sorting order ('brightest first'), interpretation of fields ('is_dark=False means daytime or twilight'), and practical implications for observation. It doesn't cover rate limits or authentication needs, but offers rich operational guidance.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured and front-loaded: purpose first, then returns, args, tips, and example. Every sentence adds value—no fluff. The tips section efficiently addresses common LLM usage scenarios without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description provides complete context: it explains the tool's behavior, parameters, return structure (including nested GeoJSON details), and practical usage tips. It covers all necessary aspects for effective tool invocation without relying on structured fields.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description fully compensates by detailing all 5 parameters with clear semantics, formats, and usage rules (e.g., 'UTC unless timezone specified', ranges for latitude/longitude, timezone offset examples). It adds meaning beyond the bare schema, making parameters fully understandable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get a complete sky summary') and resources ('all planets, moon phase, and darkness'), distinguishing it from sibling tools like get_planet_position by emphasizing it's a comprehensive alternative to multiple individual calls.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is provided: 'This is the recommended tool for "what's in the sky tonight?" questions' and 'Use this instead of calling get_planet_position 8 times.' It also mentions when to combine with weather forecasts, giving clear context for usage versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and delivers substantial behavioral context. It explains what happens when no eclipse is visible (returns specific description text), defines magnitude thresholds for eclipse types, specifies visibility conditions (sun altitude > 0), and describes the return format (GeoJSON Feature with specific properties). However, it doesn't mention error handling for invalid inputs or performance characteristics like rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and appropriately sized. It begins with a clear purpose statement, follows with detailed calculation behavior, then provides organized sections for Args, Returns, Tips, and Example. Every sentence adds value - no redundant or vague phrasing. The information is front-loaded with the core functionality stated first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 4 parameters, 0% schema coverage, no annotations, and no output schema, the description provides excellent coverage of inputs, behavior, and return format. It explains what the tool calculates, parameter constraints, interpretation of results, and includes a practical example. The only minor gap is lack of explicit error handling documentation for edge cases like invalid date formats.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given 0% schema description coverage, the description fully compensates by providing detailed parameter semantics beyond the bare schema. It explains each parameter's purpose, format constraints (date format, valid ranges for latitude/longitude/height), and the height parameter's default value. The example demonstrates proper parameter usage with realistic values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get local solar eclipse circumstances', 'Calculates whether a solar eclipse is visible') and resources ('for a specific date and location'). It distinguishes from siblings by focusing exclusively on solar eclipse calculations for a given date/location, unlike get_solar_eclipses_by_year (year-based) or get_sun_moon_data (general sun/moon data).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool versus alternatives: 'Use get_solar_eclipses_by_year first to find eclipse dates' establishes a clear workflow dependency. The 'Tips for LLMs' section includes exclusion criteria ('If description is "No Eclipse at this Location", the eclipse isn't visible here') and prerequisites ('altitude must be > 0 for eclipse to be visible').

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: the year range constraint (1800-2050), typical output volume (2-4 eclipses per year), and the return type (SolarEclipseByYearResponse). It doesn't mention error handling or rate limits, but covers essential operational context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections: purpose statement, usage guidance, args/returns documentation, practical tips, and an example. Every sentence adds value - no redundancy or fluff. The information is front-loaded with the core purpose first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter query tool with no output schema, the description provides excellent context: clear purpose, usage guidelines, parameter semantics, behavioral expectations, and an example. The 'Tips for LLMs' section adds practical guidance that helps the agent use the tool effectively within the broader toolset.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description must compensate fully. It does so by clearly explaining the 'year' parameter's purpose ('Year to query'), providing the valid range (1800-2050), and showing usage in the example. This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and resource 'list of all solar eclipses occurring in a specific year', specifying the scope (worldwide) and types (total, annular, partial, hybrid). It distinguishes from sibling get_solar_eclipse_by_date by indicating this tool returns a list for a year while that one provides detailed circumstances for a specific date.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use this tool ('to find eclipse dates') and when to use an alternative ('then use get_solar_eclipse_by_date to get detailed local circumstances'). The 'Tips for LLMs' section reinforces this workflow, providing clear guidance on tool sequencing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: timezone handling, polar region edge cases (empty data), twilight definition, and practical applications. It doesn't mention rate limits, authentication needs, or error conditions, but provides substantial operational context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with purpose statement, usage context, parameter details, return format, LLM tips, and example. Some redundancy exists (e.g., timezone sign explained twice), but overall efficient with each section serving a clear purpose. Could be slightly more front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-parameter tool with no annotations and no output schema, the description provides comprehensive coverage: clear purpose, parameter semantics, return format details (GeoJSON structure with specific data fields), edge cases, and practical usage tips. It fully compensates for the lack of structured metadata.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by providing detailed parameter documentation: format requirements (YYYY-MM-DD), ranges (-90 to 90, -180 to 180), sign conventions (negative=South/West), defaults (timezone=UTC, dst=false), and optionality. It adds significant value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'Get complete sun and moon data for one day at a specific location' with specific resources (sun and moon data) and scope (one day, specific location). It distinguishes from siblings by focusing on daily sun/moon events rather than seasons, phases, planets, or eclipses.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('Essential for planning outdoor activities, photography, navigation, and astronomical observations'), but doesn't explicitly state when to choose sibling tools like get_moon_phases or get_planet_events instead. The guidance is helpful but lacks explicit alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.